scientific article; zbMATH DE number 6982919

zbMath1467.68157arXiv1708.04622MaRDI QIDQ4558488

Alan Morningstar, R. G. Melko

Publication date: 22 November 2018

Full work available at URL: https://arxiv.org/abs/1708.04622

Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.

zbMATH Keywords

restricted Boltzmann machinedeep learningdeep belief networkdeep Boltzmann machine

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Lattice systems (Ising, dimer, Potts, etc.) and systems on graphs arising in equilibrium statistical mechanics (82B20) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (7)

Equilibrium and non-equilibrium regimes in the learning of restricted Boltzmann machines* ⋮ The Ising model with hybrid Monte Carlo ⋮ Canonical Monte Carlo multispin cluster method ⋮ Unnamed Item ⋮ Machine learning as a universal tool for quantitative investigations of phase transitions ⋮ On dissipative symplectic integration with applications to gradient-based optimization ⋮ Reconstruction of pairwise interactions using energy-based models*

Uses Software

• darch

Cites Work

• Unnamed Item
• An Efficient Learning Procedure for Deep Boltzmann Machines
• Reducing the Dimensionality of Data with Neural Networks
• Training Products of Experts by Minimizing Contrastive Divergence
• Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
• Solving the quantum many-body problem with artificial neural networks
• A Fast Learning Algorithm for Deep Belief Nets
• Discrete Restricted Boltzmann Machines
• Statistics of the Two-Dimensional Ferromagnet. Part I
• Crystal Statistics. I. A Two-Dimensional Model with an Order-Disorder Transition

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